Method and apparatus for estimating the fundamental frequency of a signal

ABSTRACT

A method and apparatus for improved pitch period (τ) estimation in a compression system is disclosed. The system uses original estimates of integer lag (τ 0 ) and open-loop prediction gain (β ol ) as input to an adaptive filter parameter initialization block ( 304 ) which supplies inputs to a plurality of adaptive filter elements ( 306-308 ). Adaptive filter elements ( 306-308 ) provide information regarding the harmonics of the residual signal (ε(n)) to an adaptive filter parameter analysis block ( 310 ). Adaptive filter parameter analysis block ( 310 ) estimates the fundamental frequency of the residual signal based on the analysis of the harmonics and outputs a pitch period (τ) for eventual use in a delay contour computation.

FIELD OF THE INVENTION

The present invention relates, in general, to communication systems and,more particularly, to coding information signals in such communicationsystems.

BACKGROUND OF THE INVENTION

Digital speech compression systems typically require estimation of thefundamental frequency of an input signal. The fundamental frequency ƒ₀is usually estimated in terms of the pitch period τ₀ (otherwise known as“lag”). The two are related by the expression $\begin{matrix}{{\tau_{0} = \frac{f_{s}}{f_{0}}},} & (1)\end{matrix}$

where the sampling frequency ƒ_(s) is commonly 8000 Hz for telephonegrade applications.

Since a speech signal is generally non-stationary, it is partitionedinto finite length vectors called frames (e.g., 10 to 40 ms), each ofwhich are presumed to be quasi-stationary. The parameters describing thespeech signal are then updated at the associated frame length intervals.The original Code Excited Linear Prediction (CELP) algorithm furtherupdates the pitch period (using what is called Long Term Prediction, orLTP) information on shorter subframe intervals, thus allowing smoothertransitions from frame to frame. It was also noted that although τ₀could be estimated using open-loop methods, far better performance wasachieved using the closed-loop approach. Closed-loop methods involve anexhaustive search of all possible values of τ₀ (typically integer valuesfrom 20 to 147) on a subframe basis, and choosing the value thatsatisfies some minimum error criterion.

An enhancement to this method involves allowing τ₀ to take on fractionalvalues. An example of a practical implementation of this method can befound in the GSM half rate speech coder, and is shown in FIG. 1. Here,lags within the range of 21 to 22⅔ are allowed ⅓ sample resolution, lagswithin the range of 23 to 34⅚ are allowed ⅙ sample resolution, and soon. In order to keep the search complexity low, a combination ofopen-loop and closed loop methods is used. The open-loop method involvesgenerating an integer lag candidate list using an autocorrelation peakpicking algorithm. The closed-loop method then searches the allowablelags in the neighborhood of the integer lag candidates for the optimalfractional lag value. Furthermore, the lags for subframes 2, 3, and 4are coded based on the difference from the previous subframe. Thisallows the lag information to be coded using fewer bits since there is ahigh intraframe correlation of the lag parameter. Even so, the GSM HRcodec uses a total of 8+(3×4)=20 bits every 20 ms (1.0 kbps) to conveythe pitch period information.

In an effort to reduce the bit rate of the pitch period information, aninterpolation strategy was developed that allows the pitch informationto be coded only once per frame (using only 7 bits→350 bps), rather thanwith the usual subframe resolution. This technique is known as relaxedCELP (or RCELP), and is the basis for the recently adopted enhancedvariable rate codec (EVRC) standard for Code Division Multiple Access(CDMA) wireless telephone systems. The basic principle is as follows.

The pitch period is estimated for the analysis window centered at theend of the current frame. The lag (delay) contour is then generated,which consists of a linear interpolation of the past frame's lag to thecurrent frame's lag. The linear prediction (LP) residual signal is thenmodified by means of sophisticated polyphase filtering and shiftingtechniques, which are designed such that the ⅛ sample interpolationboundaries are not crossed during perceptually critical instances in thewaveform. The primary reason for this residual modification process isto account for errors introduced by the open-loop integer lag estimationprocess. For example, if the integer lag is estimated to be 32 samples,when in fact the true lag is 32.5 samples, the residual waveform can bein conflict with the estimated lag by as many as 2.5 samples in a single160 sample frame. This can severely degrade the performance of the LTP.The RCELP algorithm accounts for this by shifting the residual waveformduring perceptually insignificant instances in the residual waveform(i.e., low energy) to match the delay contour. In the event that thereare no such opportunities for shifting, the shift count is accumulatedand reserved for use during the next frame. By modifying the residualwaveform to match the estimated delay contour, the effectiveness of theLTP is preserved, and the coding gain is maintained. In addition, theassociated perceptual degradations due to the residual modification areclaimed to be insignificant.

But, while this last claim may be true for medium bit rate coders suchas the EVRC full rate mode (i.e., 8.5 kbps), it is less apparent for theEVRC half rate mode, which operates at 4.0 kbps. This is because of therelative ability of the fixed codebooks to model the associated inverseerror signal. That is, if coding distortions are introduced byinefficiencies in the LTP, and those distortions can be effectivelymodeled by the fixed codebook, then the net effect is that thedistortion will be canceled. So, while the EVRC full rate mode allocates120 of 170 its per frame for fixed codebook gain and shape, the halfrate mode can afford only 42 of 80 bits per frame for the same. Thisresults in a disproportionate performance degradation due, in part, tothe fixed codebook's inability to model the coding distortion introducedby the LTP.

Therefore, there is a need for an improved method of open-loop pitchestimation that provides subsample resolution.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 generally depicts fractional lag values for a GSM half-ratespeech coder.

FIG. 2 generally depicts a speech compression system employing open-looplag estimation as is known in the prior art.

FIG. 3 generally depicts a open-loop lag estimation system in accordancewith the invention.

FIG. 4 generally shows the structure of an ith adaptive filter elementwithin the filter bank of FIG. 3.

FIG. 5 generally depicts the process of variable length sequencing,variable offset, and subsequent windowing in accordance with theinvention.

FIG. 6 generally depicts an example of an m=7 bit trained scalarquantization table in accordance with the invention.

FIG. 7 generally depicts a comparison of voiced speech lag estimationbetween a prior art method and lag estimation in accordance with theinvention.

FIG. 8 generally depicts a comparison of average absolute accumulatedshift between a prior art method and lag estimation in accordance withthe invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Stated generally, a method and apparatus for improved pitch periodestimation in a compression system is disclosed. The system usesoriginal estimates of integer lag and open-loop prediction gain as inputto an adaptive filter parameter initialization block which suppliesinputs to a plurality of adaptive filter elements. Adaptive filterelements provide information regarding the harmonics of the residualsignal to an adaptive filter parameter analysis block. Adaptive filterparameter analysis block estimates the fundamental frequency of theresidual signal based on the analysis of the harmonics and outputs apitch period for eventual use in a delay contour computation.

Stated more specifically, a method for estimating a fundamentalfrequency of a signal includes the steps of analyzing harmonics of thesignal and estimating the fundamental frequency of the signal based onthe analysis of the harmonics. In the preferred embodiment, the step ofanalyzing harmonics of the signal further comprises phase locking to theharmonics of the signal and the step of estimating further comprisesquantizing the fundamental frequency of the signal and converting thequantized fundamental frequency of the signal to the lag domain for useas a pitch period τ. In this embodiment, the signal further comprises aresidual of a speech coded signal.

FIG. 2 generally depicts a speech compression system 200 employingopen-loop lag estimation as is known in the prior art. As shown in FIG.2, the input speech signal s(n) is processed by an linear prediction(LP) analysis filter 202 which flattens the short-term spectral envelopeof input speech signal s(n). The output of the LP analysis filter isdesignated as the LP residual ε(n). The LP residual signal ε(n) is thenused by the open-loop lag estimator 204 as a basis for estimating thedelay contour τ_(c)(n) and the open-loop pitch prediction gain β_(ol).The RCELP residual modification process 206 uses this information to mapthe LP residual to the delay contour, as described above. The modifiedresidual signal is then passed through a weighted synthesis filter 207before being processed by the long term predictor 208 and eventually bythe fixed codebook 210, which characterizes the synthesizer excitationsequence. At the decoder side, the fixed codebook index/gain is input toan excitation generator 212 which outputs an excitation sequence. Theexcitation sequence is passed through long and short term synthesisfilters 214 and 216 to produce the reconstructed speech output.

In the preferred embodiment of the present invention, a more accurateestimate of the RCELP delay contour is produced which results in a moreaccurate mapping of the delay contour to the LP residual signal ε(n).FIG. 3 generally depicts a open-loop lag estimation system 300 inaccordance with the invention. As shown in FIG. 3, the LP residualsignal ε(n) is used as the input to the autocorrelation analysis block302 which uses the prior art method shown in section 4.2.3 of IS-127 as“pre-optimized” values for the integer lag τ₀ and open-loop predictiongain β_(ol). These values are then used to calculate the initialparameters for the adaptive harmonic filter bank. The filter bank isused to estimate the residual signal's fundamental frequency ƒ₀ by“phase locking” to the LP residual harmonic frequencies. The fundamentalfrequency is then quantized and converted to the lag domain for use asthe optimal pitch period τ in accordance with the invention.

To explain open-loop lag estimation in accordance with the invention, itis given [6] that the complex conjugate roots of the recursive digitalfilter are of the form: $\begin{matrix}{{H(z)} = {\frac{\gamma}{\left( {1 - {{re}^{{j\omega}_{0}}z^{- 1}}} \right)\left( {1 - {{re}^{{j\omega}_{0}}z^{- 1}}} \right)} = \frac{\gamma}{1 - {2r\quad {\cos \left( \omega_{0} \right)}z^{- 1}} + {r^{2}z^{- 2}}}}} & (2)\end{matrix}$

which results in a bandpass frequency response with center frequency ω₀.As the value of r approaches 1, the filter response becomes more andmore narrow, and the filter structure can be classified as a digitalresonator. A value of r=1 results in oscillation, and values of r>1result in instability. The value of γ that yields unity gain at thecenter frequency ω₀ is given as: $\begin{matrix}{\gamma = {\left( {1 - r} \right){\sqrt{1 + r^{2} - {2r\quad \cos \quad 2\quad \omega_{0}}}.}}} & (3)\end{matrix}$

Furthermore, by modifying H(z) to include a unit delay in the numerator:$\begin{matrix}{{H(z)} = \frac{\gamma \quad z^{- 1}}{1 - {2r\quad {\cos \left( \omega_{0} \right)}z^{- 1}} + {r^{2}z^{- 2}}}} & (4)\end{matrix}$

the phase response is such that there is a phase shift of −π/2(−90°) atthe center frequency ω₀, and an asymptotical approach to 0 and −π on thelow and high frequency sides of ω₀, respectively. This is an importantpoint because for an input signal x(n), the output signal y(n) would beorthogonal to the input when evaluated at ω₀. Extending this idea to anadaptive filter context, let us consider the following time varyingtransfer function: $\begin{matrix}{{{H\left( {q,n} \right)} = \frac{\gamma \quad q^{- 1}}{1 - {{a(n)}q^{- 1}} - {bq}^{- 2}}},} & (5)\end{matrix}$

where q⁻is a unit delay operator and n represents the sampled timeindex. For a sinusoidal input, if a(n) were allowed to vary until theoutput of the filter were orthogonal to the input, the frequency of theinput ω_(in) could be determined at time n by the relation:

ω_(in)=cos⁻¹(a(n)/2r).  (6)

This is the basic premise of the invention. The input signal ε(n) isused as an input to a filterbank of adaptive filter elements 306-308.Each of the adaptive filter elements 306-308 pre-filters the residual ata fixed harmonic frequency. FIG. 4 shows the corresponding structure ofan ith adaptive filter element, for example adaptive filter element 306,contained within the filter bank of FIG. 3. The pre-filtered output iswindowed 404 to prevent spurious signal conditions, and then used asinput to an adaptive resonator 406 of the form given in Eq. (5). Oncethe data for the current frame has been processed, the adaptive filtercoefficients for each of the harmonics is analyzed, and an estimate ofthe fundamental frequency is generated.

The LP residual signal ε(n) is first filtered by the zero-state harmonicpre-filter 402, given as:

 p _(i)(n)=γ_(1i)ε(n+j)+c _(i) p _(i)(n−1)+d _(i) p _(i)(n−2), 0≦n<L,1≦i≦N,  (7)

where N is the number of harmonics to be analyzed, and the filtercoefficients are given as: $\begin{matrix}{\left. \begin{matrix}{c_{i} = {2r_{1}\cos \quad \omega_{i}}} \\{d_{i} = {- r_{1}^{2}}}\end{matrix} \right\},{1 \leq i \leq {N.}}} & (8)\end{matrix}$

In this expression, the pole radius is given as the constant r₁=0.989and the harmonic frequency ω_(i) is given as: $\begin{matrix}{{\omega_{i} = {\frac{2\pi}{\tau_{0}}\left( {h + i - 1} \right)}},{1 \leq i \leq N},} & (9)\end{matrix}$

where τ₀ is the pre-optimized lag from autocorrelation analysis block302, h is the harmonic number corresponding to the smallest frequencygreater than the minimum frequency ƒ_(min): $\begin{matrix}{{h = \left\lceil \frac{\tau_{0}f_{\min}}{f_{s}} \right\rceil},} & (10)\end{matrix}$

and ƒf_(s) is the sampling frequency. In this embodiment, ƒ_(min)=800Hz,ƒ_(s)=8000 Hz, and N=4 harmonics. So, for example, if the lag τ₀ werechosen to be 40, the harmonic filter bank would be configured to analyzethe 4th through 7th harmonics of the fundamental frequencyƒ₀=ƒ_(s)/τ₀=200 Hz, or more explicitly 800, 1000, 1200, and 1400 Hz.

Referring back to Eqs. (3) and (7), the filter gain variable γ_(1i) canthen be calculated as:

$\begin{matrix}{{\gamma_{1i} = {\left( {1 - r_{1}} \right)\sqrt{1 + r_{1}^{2} - {2r_{1}\cos \quad 2\omega_{i}}}}},{1 \leq i \leq {N.}}} & (11)\end{matrix}$

In some applications, however, it may be more desirable (from acomplexity standpoint) to hold this value constant across the range ofallowable frequencies, however, care must be taken to assure that theadaptive filter gains are not biased as a result.

Also in Eq. (7), two symbols require definition. First, the sequencelength L is defined such that at least three full pitch periods must becontained within the LP residual signal ε(n), up to a given maximum.This guarantees a meaningful input to the adaptive filters 306-308. Thesequence length L can then be defined as: $\begin{matrix}{L = \left\{ \begin{matrix}{{3{L_{f}/2}},} & {\tau_{0} \leq {L_{f}/2}} \\{{3\tau_{0}},} & {{{L_{f}/2} < \tau_{0} < {2{L_{f}/3}}},} \\{{2L_{f}},} & {otherwise}\end{matrix} \right.} & (12)\end{matrix}$

where L_(ƒ)=160 is the frame length of this embodiment. The associatedvariable offset j into the input sequence ε(n) can then be expressed as:

$\begin{matrix}{j = \left\{ \begin{matrix}{{L_{f}/2},} & {\tau_{0} \leq {L_{f}/2}} \\{{{2L_{f}} - {3\tau_{0}}},} & {{L_{f}/2} < \tau_{0} < {2{L_{f}/3.}}} \\{0,} & {otherwise}\end{matrix} \right.} & (13)\end{matrix}$

The process of variable length sequencing, variable offset, andsubsequent windowing can be more readily observed in FIG. 5. In thisembodiment, the LPC residual sequence ε(n) consists of 320 samples ofinformation, the first 80 of which represent the last half of the lastframe of information. These are used to “prime” the state of the LPanalysis filter 202 and are used here to extend the pitch analysis framefor low frequencies. The next 160 samples represent the current frame ofinformation, and the following 80 samples represent the “look-ahead” tothe next frame. Since RCELP attempts to interpolate the lag fromframe-to-frame, it is desirable to estimate the lag corresponding to thepoint at the end of the current frame. To do this adequately, a“look-ahead” to the next frame thereby centering the analysis frametowards the end of the current frame must occur. In the event that theinitial lag is greater than half a frame length, however, the analysisframe is elongated towards past samples. This is a conscious effort tonot increase algorithmic delay by not increasing the length of thelook-ahead. Using this method, the sequence length L is equal to 240 forlags ≦80. For lags >80 and ≦120, the analysis window is stretched backin time successively until the beginning of the look-back of theprevious frame is reached.

Also implicit in FIG. 5 is the windowing 404 of the pre-filtered outputsequence p_(i)(n), which can be expressed as:

x _(i)(n)=w(n)p _(i)(n), 0≦n<L, 1≦i≦N,  (14)

where ω(n) is the window: $\begin{matrix}{{w(n)} = {{w\left( {L - 1 - n} \right)} = \left\{ {\begin{matrix}{{\sin^{2}\left( {{\pi \left( {n + 0.5} \right)}/L_{f}} \right)},} & {0 \leq n < {L_{f}/2}} \\{1,} & {{L_{f}/2} \leq n < {L/2}}\end{matrix}.} \right.}} & (15)\end{matrix}$

The window ω(n) can be described as a smoothed trapezoid window. Otherwindow types may be used with varying degrees of performance, however,keeping the window “tails” constant is a computational advantage sinceonly L_(ƒ)/2 samples need be stored or calculated. Other window typesthat have dependence on L in the trigonometric function e.g.,sin²(π(n+0.5)/L)) would require recalculation and/or different storagememory for each value of L.

The windowed pre-filter output x(n) is then used as input to azero-state adaptive harmonic resonator 406 of the form:

y _(i)(n)=γ_(2i) x(n)+a_(i)(n)y _(i)(n−1)+b _(i) y _(i)(n−2), 0≦n<L,1≦i≦N,  (16)

where the fundamental difference between this and the pre-filter in Eq.(7) is that the filter coefficient corresponding to the filter resonantfrequency a_(i)(n) in Eq. (16) is allowed to vary with time. The initialcoefficient values are calculated as in Eq.(8) as: $\begin{matrix}{\left. \begin{matrix}{{a_{i}(0)} = {2r_{2}\cos \quad \omega_{i}}} \\{b_{i} = {- r_{2}^{2}}}\end{matrix} \right\},{1 \leq i \leq {N.}}} & (17)\end{matrix}$

where the pole radius is r₂=0.989 and the resonant frequency ω_(i) isgiven in Eqs. (9) and (10). The filter gain term is also calculated asin Eq. (11) by: $\begin{matrix}{{\gamma_{2i} = {\left( {1 - r_{2}} \right)\sqrt{1 + r_{2}^{2} - {2r_{2}\cos \quad 2\omega_{i}}}}},{1 \leq i \leq {N.}}} & (18)\end{matrix}$

As the windowed pre-filter sequence is passed through the second filter406, which in the preferred embodiment is an adaptive harmonic resonator406, the resonant frequency coefficient for each harmonic is modifiedaccording to:

a _(i)(n+1)=a _(i)(n)+α_(i) x _(i)(n)y _(i)(n−1), 0≦n>L, 1≦i≦N,  (19)

where a_(i) is the normalized adaptation gain given by: $\begin{matrix}{{\alpha_{i} = \frac{\alpha_{1}\beta_{ol}}{k + {\sum\limits_{n = 0}^{L - 1}{x_{i}^{2}(n)}}}},{1 \leq i \leq {N.}}} & (20)\end{matrix}$

In this embodiment, the gain constant α₁=0.49, the reciprocal limitingconstant k=150, and the open-loop prediction gain (obtained from theautocorrelation analysis block in FIG. 3) has the range 0≦β_(ol)≦1.These values assume the input speech to be normalized within the 16 bitrange ±32767. Also in this embodiment, the gain term depends on theentire L samples to be passed through the pre-filter/window prior to theexecution of the adaptive filter.

Once all L samples have been passed through the adaptive harmonic filter406, the coefficients a_(i)(L) can be analyzed for deviation from theinitial center frequencies. The inverse of Eqs. (8) and (9) can beapplied to each of the harmonic elements of the filter bank to form thefollowing estimation of the unquantized fundamental frequency ƒ₀:$\begin{matrix}{{f_{0} = {\frac{f_{s}}{2\pi}{\sum\limits_{i = 1}^{N}{{\lambda (i)}\frac{\cos^{- 1}\left( {{{a_{i}(L)}/2}r_{2}} \right)}{h + i - 1}}}}},} & (21)\end{matrix}$

where λ(i) is a weighting function that appropriately weights theimportance of each of the harmonic elements, such that the sum of allelements of λ(i) equal unity. In this embodiment, λ(i) is equal to alinear average, or λ(i)=1 /N, 1≦i≦N. λ(i) is highly dependent on theinput data; other functions may or may not yield better performance.

The quantized value of the fundamental frequency ƒ* is then found bychoosing the value of the index k that minimizes the following:

min {(ƒ₀−ƒ_(table)(k))²}, 0≦k>2^(m),  (22)

where f_(table)(k) are the allowable values of the quantized fundamentalfrequency and m is the number of bits allocated to the lag parameter. Anexample of an m=7 bit trained scalar quantization table is given in FIG.6.

The quantized fundamental frequency ƒ* can then be used to generate thecorresponding delay contour τ_(c)(n) which is output from the lagcontour computation block 312 of FIG. 3, for which the following exampleis given as: $\begin{matrix}{{{\tau_{c}(n)} = \begin{Bmatrix}{{{\tau \left( {m - 1} \right)} + {\left( {n\left( {{\tau \left( {m - 1} \right)} - {\tau (m)}} \right)} \right)/L_{f}}},} & {{{{\tau \left( {m - 1} \right)} - {\tau (m)}}} < 16} \\{{\tau (m)},} & {otherwise}\end{Bmatrix}},} & (23) \\{0 \leq n < L_{f}} & \quad\end{matrix}$

where τ=ƒ_(s)/ƒ*, m represents the current frame and m−1 is the previousframe. In this equation, the interpolated lag is considered valid onlyif the trajectory does not change too abruptly.

In the preferred embodiment, a database of over 80,000 frames of speechand music signals was used to generate the data shown in FIG. 6 based onthe dataset's probability density function. While this data isstatistically optimal over the various fundamental frequency values fromthe training set, it is interesting to note that the distribution moreclosely reflects the properties of the human auditory system. That is,psychoacoustics principles reveal that the critical bands of hearing areuniform in frequency below 500 Hz; in the prior art open-loop lagestimator shown in FIG. 2, the distribution of quantization range isuniform over pitch period, which is inversely proportional to frequency.Thus, for a given fundamental frequency range of say 66 to 400 Hz, thepsychoacoustically optimal distribution for a 7 bit quantizer wouldconsist of a uniform frequency distribution spaced at 2.6 Hz intervals.The table shown in FIG. 6 yields relatively constant spacing throughabout 250 Hz (τ=32), but then sharply increases to about 20 Hz at theend frequency of about 400 Hz (τ≈20). This decrease in resolution is dueto the diminished probability of encountering very high frequencytalkers. So, the present invention facilitates the combination of bothstatistically and psychoacoustically joint optimal datasets by allowingarbitrary quantization levels for the fundamental frequencies. This isnot readily achievable in the prior art.

The support for objective improvement can be observed in FIG. 7, wherethe lag trajectory for a short passage of strongly voiced speech isshown. While the prior art shows distinct “staircase” effects duringtransitional (frames 52 to 58 ) and steady state (frames 37 to 45 )passages, the lag estimation in accordance with the inventioneffectively smoothes out the rough edges associated with the integer lagboundaries.

As described above, improvements to the RCELP algorithm can be evaluatedobjectively by measuring the accumulated shift that results from theinability of the LP residual signal ε(n) to be appropriately mapped tothe estimated delay contour. Since one purpose of the preferredembodiment in accordance with the invention is to more accuratelyestimate the RCELP delay contour, the efficiency of the RCELP algorithmis improved since lag estimation in accordance with the inventionrequires less error to be tolerated by lowering the accumulated averageshift factor. The improvement can be observed in FIG. 8, which wasgenerated from a 80,000+ frame database.

Additionally, the subjective performance improvement is highly audible.Testing shows consistent preference to lag estimation in accordance withthe invention during blind A/B tests, and it is estimated that theinventive method and apparatus provides 0.1 to 0.2 Mean Opinion Score(MOS) points improvement in Absolute Category Rating (ACR) tests whenused with the EVRC half-rate maximum mode of operation (4.0 kbps).

While the invention has been particularly shown and described withreference to a particular embodiment, it will be understood by thoseskilled in the art that various changes in form and details may be madetherein without departing from the spirit and scope of the invention.For example, one skilled in the art will recognize that lag estimationin accordance with the invention can additionally benefit other, moregeneral algorithms/vocoders which require accurate open-loop estimationof the fundamental frequency of an input signal. Suchalgorithms/vocoders include, but are not limited to, harmonic vocoders,sinusoidal transform coders (STC), and homomorphic vocoders. In additionto cellular communication systems, other applications which may benefitinclude digital hearing aids, audio speech coders, voice mail systems,etc. The corresponding structures, materials, acts and equivalents ofall means or step plus function elements in the claims below areintended to include any structure, material, or acts for performing thefunctions in combination with other claimed elements as specificallyclaimed.

What we claim is:
 1. In an open-loop lag estimation system for use in aspeech compression system, a method for estimating a fundamentalfrequency with improved pitch period estimation of a linear predictionresidual signal, the method comprising the steps of: receiving thelinear prediction residual signal; generating an integer lag and anopen-loop prediction gain of the linear prediction residual signal;generating a plurality of initial parameters using the integer lag andthe open-loop prediction gain; estimating the fundamental frequency ofthe linear prediction residual signal using the plurality of initialparameters.
 2. A method as recited in claim 1, wherein the estimatingstep comprises phase locking to a plurality of linear predictionresidual harmonic frequencies of the linear prediction residual signal.3. A method as recited in claim 2, wherein the estimating e furthercomprises quantizing the fundamental frequency of the linear predictionresidual signal and converting the quantized fundamental frequency ofthe linear prediction residual signal to a lag domain for use as a pitchperiod.
 4. In a speech compression system, an open-loop lag estimationsystem for estimating a fundamental frequency with improved pitch periodof a linear prediction residual signal, wherein the open-loop lagestimation system comprises: an autocorrelation analysis block forreceiving the linear prediction residual signal, wherein theautocorrelation analysis block produces an integer lag and an open-loopprediction gain; an adaptive filter parameter initialization blockcoupled to the autocorrelation analysis block and receiving the integerlag and the open-loop prediction gain, wherein the adaptive filterparameter initialization block produces a plurality of initialparameters; an adaptive harmonic filter bank coupled to the adaptivefilter parameter initialization block, wherein the adaptive harmonicfilter bank receives the plurality of initial parameters, and furtherwherein the adaptive harmonic filter bank estimates the fundamentalfrequency of the linear prediction residual signal using the pluralityof initial parameters.
 5. An open-loop lag estimation system as recitedin claim 4, wherein the adaptive harmonic filter bank estimates thefundamental frequency of the linear prediction residual signal by phaselocking to a plurality of linear prediction residual harmonicfrequencies of the linear prediction residual signal.
 6. An open-looplag estimation system as recited in claim 5, wherein the adaptiveharmonic filter bank further quantizes the fundamental frequency of thelinear prediction residual signal, and further wherein the adaptiveharmonic filter bank converts the quantized fundamental frequency of thelinear prediction residual signal to a lag domain for use as a pitchperiod.